基于遗传算法的大学生就业信心指数组合预测

大学生的就业信心指数的变化趋势,可作为高校制定学生工作计划、应对当前就业形势压力的参考依据,信心指数预测结果的准确性直接关系到政策制定与实施的效果。在分析自回归、神经网络及灰色系统等单预测模型的优点与不足基础上,提出综合利用各单模型预测信息的组合预测思路,构建基于遗传算法和信息熵求解单模型权重的组合预测模型。预测结果显示,组合预测模型在拟合期的表现与神经网络模型接近,优于其它两种模型;在预测期远超过其它模型的预测效果。组合预测模型的拟合性能和泛化性能优越,预测信息可作为高校制定相关政策时的重要参考依据。

Combination Forecasting of the Employment Confidence Index of College Studentby Genetic Algorithm

Employment confidence index of college student (ECI) can be used as a reference to assist the university administrators to formulate work plans and cope with the current employment situation. So, the most accurate forecasting results are needed, which is directly related to the effect of policy formulation and implementation. In order to solve this problem, the advantages and disadvantages of the model of threshold auto-regression (TAR), back propagation (BP) and gray model were analyzed. According to analysis result, we propose a combined forecast model based on the genetic algorithm (CF-GA), in which GA is used to determinate the weight of each single model. The forecast results show that the performance of CF-GA has a good behavior to other three models, which can be used as an important reference for the university development of relevant policies.